The invention pertains to machine vision and, more particularly, to methods for calibrating the reference frame of a camera to that of a moveable object (e.g., a motion stage) imaged by the camera.
Machine vision refers to the automated analysis of an image to determine characteristics of objects and other features shown in the image. It is often employed in automated manufacturing lines, where images of components are analyzed to determine placement and alignment prior to assembly. Machine vision is also used for quality assurance. For example, in the semiconductor device industry, images of chips are analyzed to insure that leads, solder paste and other components do not overrun designated boundaries.
In many machine vision applications, it is essential to correlate locations in the "real world," e.g., on a motion stage or conveyor belt, with coordinates in an image. For example, a camera image of a part being assembled on a robotic assembly line may reveal that a component is misplaced by several pixels. In order to move the motion stage so that the object can be properly repositioned, the relationship between coordinates in the image and on the motion stage must be known. That relationship is known as the calibration relationship.
The prior art suggests the use of so-called calibration plates to determine the calibration relationship between a camera and an object. A typical calibration plate consists of a complex "target," such as an array of dots, a checkerboard, a bulls-eye of concentric circles, or a set of parallel stripes, that is placed on the object. Traditionally, it has been important to construct and reproduce these plates carefully because any error in the target will be wrongly "corrected for" when the camera is calibrated. For example, if the circles in a bulls eye-type target are slightly eccentric, the resulting calibration may produce an incorrect aspect ratio (i.e., the ratio of width to height).
It is sometimes quite difficult to construct an accurate calibration target. This is particularly true when the camera magnification is very large and the corresponding calibration target is very small. Here, even a small deviation in the target will result in an incorrect calibration relationship. This problem is exacerbated in machine vision systems that utilize multiple cameras to image a single target, e.g., systems of the type used in semiconductor chip manufacture, in which two or more high resolution cameras are used to inspect, simultaneously, multiple disparate regions on the chip surface. In addition to the difficulties associated with calibrating the reference frame of a single camera to the real world reference frame of the chip surface (or motion stage), are those associated with calibrating the reference frames of the cameras to one another.
An object of this invention is to provide improved machine vision systems and, particularly, improved machine visions methods for calibrating the reference frame of a camera to that of a moveable object (e.g., a motion stage) imaged by the camera.
Another object of the invention is to provide such methods as can calibrate the reference frames of multiple cameras to each other, as well as to that of a moveable object imaged by the cameras.
Yet another object of the invention is to provide such methods as minimize reliance on precisely machined calibration targets and plates.
Yet still another object of the invention is to provide such methods as can be implemented on conventional digital data processors or other conventional machine vision analysis equipment.
Still yet another object of the invention is to calibrate the reference frames of multiple cameras with respect to the motion stage's center of rotation.
Yet still another object of the invention is to provide such methods that can rapidly determine calibration relationships without undue consumption of resources.